Automatic extrinsic calibration of the coach in robocup

In 2014, a coaching robot was added to the Robocup Standard Platform League rules. This coach robot sits on top of a table outside of the playing field. Up until now, no real efforts were made to implement this additional robot. The same perception processes used on the existing field robots could not be directly imported onto the coach robot, due to its heightened position. This project realizes a new approach for self-localization on the coach robot, which is a crucial process for the perception module. Various calculations regarding the ball, field and goal require a localized robot to give a correct understanding of the game state.
Using prior knowledge of the sizes and position of playing field lines, we implement a rudimentary self-localization process. In conjunction with the values detected using the robot camera, we can deduce the robot position. To detect the necessary field lines, we adapt a random forest classifier algorithm that was implemented in a previous semester project used for ball detection. In a series of evaluations, we show the limits of this approach and the achieved accuracy to be satisfactory.